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That must have been a long time back. Having lived through the time when web pages were served through CGI and mobile phones only existed in movies, when SVMs w

That must have been a long time back. Having lived through the time when web pages were served through CGI and mobile phones only existed in movies, when SVMs where the new hotness in ML and people would write about how weird NNs were, I feel like I've seen a lot more concrete progress in the last few decades than this year. This year honestly feels quite stagnant. LLMs are literally technology that can only reproduce the past. They're cool, but they were way cooler 4 years ago. We've taken big ideas like "agents" and "reinforcement learning" and basically stripped them of all meaning in order to claim progress. I mean, do you remember Geoffrey Hinton's RBM talk at Google in 2010? [0] That was absolutely insane for anyone keeping up with that field. By the mid-twenty teens RBMs were already outdated. I remember when everyone was implementing flavors of RNNs and LSTMs. Karpathy's character 2015 RNN project was insane [1]. This comment makes me wonder if part of the hype around LLMs is just that a lot of software people simply weren't paying attention to the absolutely mind-blowing progress we've seen in this field for the last 20 years. But even ignoring ML, the world's of web development and mobile application development have gone through incredible progress over the last decade and a half. I remember a time when JavaScript books would have a section warning that you should never use JS for anything critical to the application. Then there's the work in theorem provers over the last decade... If you remember when syntactic sugar was progress, either you remember way further back than I do, or you weren't paying attention to what was happening in the larger computing world. 0. https://www.youtube.com/watch?v=VdIURAu1-aU https://www.youtube.com/watch?v=VdIURAu1-aU 1. https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://karpathy.github.io/2015/05/21/rnn-effectiveness/

handoflixue 10mo ago
> LLMs are literally technology that can only reproduce the past. Funny, I've used them to create my own personalized text editor, perfectly tailored to what I actually want. I'm pretty sure that didn't exist before. It's wild to me how many people who talk about LLM apparently haven't learned how to use them for even very basic tasks like this! No wonder you think they're not that powerful, if you don't even know basic stuff like this. You really owe it to yourself to try them out.
- crystal_revenge 10mo ago
  > You really owe it to yourself to try them out. I've worked at multiple AI startups in lead AI Engineering roles, both working on deploying user facing LLM products and working on the research end of LLMs. I've done collaborative projects and demos with a pretty wide range of big names in this space (but don't want to doxx myself too aggressively), have had my LLM work cited on HN multiple times, have LLM based github projects with hundreds of stars, appeared on a few podcasts talking about AI etc. This gets to the point I was making. I'm starting to realize that part of the disconnect between my opinions on the state of the field and others is that many people haven't really been paying much attention. I can see if recent LLMs are your first intro to the state of the field, it must feel incredible.
  - CamperBob2 10mo ago
    That's all very impressive, to be sure. But are you sure you're getting the point? As of 2025, LLMs are now very good at writing new code, creating new imagery, and writing original text. They continue to improve at a remarkable rate. They are helping their users create things that didn't exist before. Additionally, they are now very good at searching and utilizing web resources that didn't exist at training time. So it is absurdly incorrect to say "they can only reproduce the past." Only someone who hasn't been paying attention (as you put it) would say such a thing.
    - crystal_revenge 10mo ago
      I think the confusion is people's misunderstanding of what 'new code' and 'new imagery' mean. Yes, LLMs can generate a specific CRUD webapp that hasn't existed before but only based on interpolating between the history of existing CRUD webapps. I mean traditional Markov Chains can also produce 'new' text in the sense that "this exact text" hasn't been seen before, but nobody would argue that traditional Markov Chains aren't constrained by "only producing the past". This is even more clear in the case of diffusion models (which I personally love using, and have spent a lot of time researching). All of the "new" images created by even the most advanced diffusion models are fundamentally remixing past information. This is really obvious to anyone who has played around with these extensively because they really can't produce truly novel concepts. New concepts can be added by things like fine-tuning or use of LoRAs, but fundamentally you're still just remixing the past. LLMs are always doing some form of interpolation between different points in the past. Yes they can create a "new" SQL query, but it's just remixing from the SQL queries that have existed prior. This still makes them very useful because a lot of engineering work, including writing a custom text editor, involve remixing existing engineering work. If you could have stack-overflowed your way to an answer in the past, an LLM will be much superior. In fact, the phrase "CRUD" largely exists to point out that most webapps are fundamentally the same. A great example of this limitation in practice is the work that Terry Tao is doing with LLMs. One of the largest challenges in automated theorem proving is translating human proofs into the language of a theorem prover (often Lean these days). The challenge is that there is not very much Lean code currently available to LLMs (especially with the necessary context of the accompanying NL proof), so they struggle to correctly translate. Most of the research in this area is around improving LLM's representation of the mapping from human proofs to Lean proofs (btw, I personally feel like LLMs do have a reasonably good chance of providing major improvements in the space of formal theorem proving, in conjunction with languages like Lean, because the translation process is the biggest blocker to progress). When you say: > So it is absurdly incorrect to say "they can only reproduce the past." It's pretty clear you don't have a solid background in generative models, because this is fundamentally what they do: model an existing probability distribution and draw samples from that. LLMs are doing this for a massive amount of human text, which is why they do produce some impressive and useful results, but this is also a fundamental limitation. But a world where we used LLMs for the majority of work, would be a world with no fundamental breakthroughs. If you've read The Three Body Problem, it's very much like living in the world where scientific progress is impeded by sophons. In that world there is still some progress (especially with abundant energy), but it remains fundamentally and deeply limited.
waldrews 10mo ago
I'm being hyperbolic of course, but I'm a little dismissive of the progress that happened since the days of BBS's and car based cell phones - we just got more connectivity, more capacity, more content, bigger/faster. Likewise, my attitude toward machine learning before 2023 is a smug 'heh, these computer scientists are doing undisciplined statistics at scale, how nice for them.' Then all of a sudden the machines woke up and started arguing with me, coherently, even about niche topics I have a PhD in. I can appreciate in retrospect how much of the machine learning progress ultimately went into that, but, like fusion, the magic payoff was supposed to be decades away and always remain decades away. This wasn't supposed to happen in my lifetime. 2025 progress isn't the 2023 shock, but this was the year LLM's-as-programmers (and LLM's-as-mathematicians, and...) went from 'isn't that cute, the machine is trying' to 'an expert with enough time would make better choices than the machine did,' and that makes for a different world. More so than, going from a Commodore Vic 20 with 4k of RAM and a modem to the latest Macbook.
ako 10mo ago
> This year honestly feels quite stagnant. LLMs are literally technology that can only reproduce the past. Is this such a big limitation? Most jobs are basically people trained on past knowledge applying it today. No need to generate new knowledge. And a lot of new knowledge is just combining 2 things from the past in a new way.
- mr_toad 10mo ago
  Most people are capable of long-term learning. Some people are capable of discovering and inventing new things. I think the two are related, and current NN architecture doesn’t allow this. An AI that can cobble together a CRUD application to spec is one thing. An AI that can come up with a new idea for a successful app on its own is a completely different ball game.
HarHarVeryFunny 10mo ago
> LLMs are literally technology that can only reproduce the past. That's incorrect on many levels. They are drawing upon, and reproducing, language patterns from "the past", but they are combining those patterns in ways that may have never have been seen before. They may not be truly creative, but they are still capable of generating novel outputs. > They're cool, but they were way cooler 4 years ago. Maybe this year has been more about incremental progress with LLMs than the shock/coolness factor of talking to an LLM for the first time, but the utility of them, especially for programming, has dramatically increased this year, really in the last 6 months. The improvement in "AI" image and video generation has also been impressive, to the point now that fake videos on YouTube can often only be identified as such by common sense rather that the fact that they don't look real. Incremental improvement can often be more impressive that innovation, whose future importance can be hard to judge when it first appears. How many people read "Attention is all you need" in 2017 and thought "Wow! This is going to change the world!". Not even the authors of the paper thought that.

handoflixue 10mo ago

> LLMs are literally technology that can only reproduce the past. Funny, I've used them to create my own personalized text editor, perfectly tailored to what I actually want. I'm pretty sure that didn't exist before. It's wild to me how many people who talk about LLM apparently haven't learned how to use them for even very basic tasks like this! No wonder you think they're not that powerful, if you don't even know basic stuff like this. You really owe it to yourself to try them out.

crystal_revenge 10mo ago
> You really owe it to yourself to try them out. I've worked at multiple AI startups in lead AI Engineering roles, both working on deploying user facing LLM products and working on the research end of LLMs. I've done collaborative projects and demos with a pretty wide range of big names in this space (but don't want to doxx myself too aggressively), have had my LLM work cited on HN multiple times, have LLM based github projects with hundreds of stars, appeared on a few podcasts talking about AI etc. This gets to the point I was making. I'm starting to realize that part of the disconnect between my opinions on the state of the field and others is that many people haven't really been paying much attention. I can see if recent LLMs are your first intro to the state of the field, it must feel incredible.
- CamperBob2 10mo ago
  That's all very impressive, to be sure. But are you sure you're getting the point? As of 2025, LLMs are now very good at writing new code, creating new imagery, and writing original text. They continue to improve at a remarkable rate. They are helping their users create things that didn't exist before. Additionally, they are now very good at searching and utilizing web resources that didn't exist at training time. So it is absurdly incorrect to say "they can only reproduce the past." Only someone who hasn't been paying attention (as you put it) would say such a thing.
  - crystal_revenge 10mo ago
    I think the confusion is people's misunderstanding of what 'new code' and 'new imagery' mean. Yes, LLMs can generate a specific CRUD webapp that hasn't existed before but only based on interpolating between the history of existing CRUD webapps. I mean traditional Markov Chains can also produce 'new' text in the sense that "this exact text" hasn't been seen before, but nobody would argue that traditional Markov Chains aren't constrained by "only producing the past". This is even more clear in the case of diffusion models (which I personally love using, and have spent a lot of time researching). All of the "new" images created by even the most advanced diffusion models are fundamentally remixing past information. This is really obvious to anyone who has played around with these extensively because they really can't produce truly novel concepts. New concepts can be added by things like fine-tuning or use of LoRAs, but fundamentally you're still just remixing the past. LLMs are always doing some form of interpolation between different points in the past. Yes they can create a "new" SQL query, but it's just remixing from the SQL queries that have existed prior. This still makes them very useful because a lot of engineering work, including writing a custom text editor, involve remixing existing engineering work. If you could have stack-overflowed your way to an answer in the past, an LLM will be much superior. In fact, the phrase "CRUD" largely exists to point out that most webapps are fundamentally the same. A great example of this limitation in practice is the work that Terry Tao is doing with LLMs. One of the largest challenges in automated theorem proving is translating human proofs into the language of a theorem prover (often Lean these days). The challenge is that there is not very much Lean code currently available to LLMs (especially with the necessary context of the accompanying NL proof), so they struggle to correctly translate. Most of the research in this area is around improving LLM's representation of the mapping from human proofs to Lean proofs (btw, I personally feel like LLMs do have a reasonably good chance of providing major improvements in the space of formal theorem proving, in conjunction with languages like Lean, because the translation process is the biggest blocker to progress). When you say: > So it is absurdly incorrect to say "they can only reproduce the past." It's pretty clear you don't have a solid background in generative models, because this is fundamentally what they do: model an existing probability distribution and draw samples from that. LLMs are doing this for a massive amount of human text, which is why they do produce some impressive and useful results, but this is also a fundamental limitation. But a world where we used LLMs for the majority of work, would be a world with no fundamental breakthroughs. If you've read The Three Body Problem, it's very much like living in the world where scientific progress is impeded by sophons. In that world there is still some progress (especially with abundant energy), but it remains fundamentally and deeply limited.

waldrews 10mo ago

I'm being hyperbolic of course, but I'm a little dismissive of the progress that happened since the days of BBS's and car based cell phones - we just got more connectivity, more capacity, more content, bigger/faster. Likewise, my attitude toward machine learning before 2023 is a smug 'heh, these computer scientists are doing undisciplined statistics at scale, how nice for them.' Then all of a sudden the machines woke up and started arguing with me, coherently, even about niche topics I have a PhD in. I can appreciate in retrospect how much of the machine learning progress ultimately went into that, but, like fusion, the magic payoff was supposed to be decades away and always remain decades away. This wasn't supposed to happen in my lifetime. 2025 progress isn't the 2023 shock, but this was the year LLM's-as-programmers (and LLM's-as-mathematicians, and...) went from 'isn't that cute, the machine is trying' to 'an expert with enough time would make better choices than the machine did,' and that makes for a different world. More so than, going from a Commodore Vic 20 with 4k of RAM and a modem to the latest Macbook.

ako 10mo ago

> This year honestly feels quite stagnant. LLMs are literally technology that can only reproduce the past. Is this such a big limitation? Most jobs are basically people trained on past knowledge applying it today. No need to generate new knowledge. And a lot of new knowledge is just combining 2 things from the past in a new way.

mr_toad 10mo ago
Most people are capable of long-term learning. Some people are capable of discovering and inventing new things. I think the two are related, and current NN architecture doesn’t allow this. An AI that can cobble together a CRUD application to spec is one thing. An AI that can come up with a new idea for a successful app on its own is a completely different ball game.

HarHarVeryFunny 10mo ago

> LLMs are literally technology that can only reproduce the past. That's incorrect on many levels. They are drawing upon, and reproducing, language patterns from "the past", but they are combining those patterns in ways that may have never have been seen before. They may not be truly creative, but they are still capable of generating novel outputs. > They're cool, but they were way cooler 4 years ago. Maybe this year has been more about incremental progress with LLMs than the shock/coolness factor of talking to an LLM for the first time, but the utility of them, especially for programming, has dramatically increased this year, really in the last 6 months. The improvement in "AI" image and video generation has also been impressive, to the point now that fake videos on YouTube can often only be identified as such by common sense rather that the fact that they don't look real. Incremental improvement can often be more impressive that innovation, whose future importance can be hard to judge when it first appears. How many people read "Attention is all you need" in 2017 and thought "Wow! This is going to change the world!". Not even the authors of the paper thought that.